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A cnn prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data
Caesarendra, W., Triwiyanto, T., Pandiyan, V., Glowacz, A., Permana, S. D. H., & Tjahjowidodo, T. (2021). A cnn prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data. Electronics, 10(12), 1429. https://doi.org/10.3390/electronics10121429
Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope
Deshpande, P., Pandiyan, V., Meylan, B., & Wasmer, K. (2021). Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope. Wear, 203622 (12 pp.). https://doi.org/10.1016/j.wear.2021.203622
Artificial intelligence for monitoring and control of metal additive manufacturing
Masinelli, G., Shevchik, S. A., Pandiyan, V., Quang-Le, T., & Wasmer, K. (2021). Artificial intelligence for monitoring and control of metal additive manufacturing. In M. Meboldt & C. Klahn (Eds.), Industrializing additive manufacturing. Proceedings of AMPA2020 (pp. 205-220). https://doi.org/10.1007/978-3-030-54334-1_15
Analysis of time, frequency and time-frequency domain features from acoustic emissions during laser powder-bed fusion process
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Logé, R., & Wasmer, K. (2020). Analysis of time, frequency and time-frequency domain features from acoustic emissions during laser powder-bed fusion process. In M. Schmidt, F. Vollertsen, & E. Govekar (Eds.), Procedia CIRP: Vol. 94. 11th CIRP conference on photonic technologies [LANE 2020] (pp. 392-397). https://doi.org/10.1016/j.procir.2020.09.152
Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review
Pandiyan, V., Shevchik, S., Wasmer, K., Castagne, S., & Tjahjowidodo, T. (2020). Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review. Journal of Manufacturing Processes, 57, 114-135. https://doi.org/10.1016/j.jmapro.2020.06.013
Modelling of material removal in abrasive belt grinding process: a regression approach
Pandiyan, V., Caesarendra, W., Glowacz, A., & Tjahjowidodo, T. (2020). Modelling of material removal in abrasive belt grinding process: a regression approach. Symmetry, 12(1), 99 (27 pp.). https://doi.org/10.3390/SYM12010099